A Sensitized Plastic Fiber Sensor for Multi-Point Bending Measurement Based on Deep Learning
As we all know, the change of mode interference caused by the curvature change in multi-mode fiber (MMF) can be well represented as a fiber specklegram recorded by CCD (Charge-coupled Device). However, it's difficult to identify the different bending occurred in several points in short di...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9511279/ |
| _version_ | 1818215271783989248 |
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| author | Shun Lu Zhongwei Tan Guangde Li Yang Jingya |
| author_facet | Shun Lu Zhongwei Tan Guangde Li Yang Jingya |
| author_sort | Shun Lu |
| collection | DOAJ |
| description | As we all know, the change of mode interference caused by the curvature change in multi-mode fiber (MMF) can be well represented as a fiber specklegram recorded by CCD (Charge-coupled Device). However, it's difficult to identify the different bending occurred in several points in short distance. The paper proposes plastic fiber bending sensors can be used to detect the multi-point bending without adding any hardware. The convolutional neural network was used to classify the output speckles under different bending states. Specklegrams from fiber with three sensitization areas can be recognized by the neural network with a bending interval of 15°,10° and 5° with an accuracy rate of 99.2%, 96.1% and 93.5% respectively. Compared with traditional multi-point distributed sensors, this method is lower cost and easier to operate. The method proposed in this paper can find applications in distinguishing the status of certain structures, such as robotic arms and some disabled auxiliary equipment. |
| first_indexed | 2024-12-12T06:33:26Z |
| format | Article |
| id | doaj.art-c5607300e10642fda469afcf36b93079 |
| institution | Directory Open Access Journal |
| issn | 1943-0655 |
| language | English |
| last_indexed | 2024-12-12T06:33:26Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj.art-c5607300e10642fda469afcf36b930792022-12-22T00:34:32ZengIEEEIEEE Photonics Journal1943-06552021-01-011351710.1109/JPHOT.2021.31035669511279A Sensitized Plastic Fiber Sensor for Multi-Point Bending Measurement Based on Deep LearningShun Lu0https://orcid.org/0000-0003-0237-2265Zhongwei Tan1Guangde Li2Yang Jingya3Key Lab of All Optical Network & Advanced Telecommunication Network Ministry of Education, Institute of Lightwave Technology, Beijing Jiaotong University, Beijing, ChinaKey Lab of All Optical Network & Advanced Telecommunication Network Ministry of Education, Institute of Lightwave Technology, Beijing Jiaotong University, Beijing, ChinaKey Lab of All Optical Network & Advanced Telecommunication Network Ministry of Education, Institute of Lightwave Technology, Beijing Jiaotong University, Beijing, ChinaKey Lab of All Optical Network & Advanced Telecommunication Network Ministry of Education, Institute of Lightwave Technology, Beijing Jiaotong University, Beijing, ChinaAs we all know, the change of mode interference caused by the curvature change in multi-mode fiber (MMF) can be well represented as a fiber specklegram recorded by CCD (Charge-coupled Device). However, it's difficult to identify the different bending occurred in several points in short distance. The paper proposes plastic fiber bending sensors can be used to detect the multi-point bending without adding any hardware. The convolutional neural network was used to classify the output speckles under different bending states. Specklegrams from fiber with three sensitization areas can be recognized by the neural network with a bending interval of 15°,10° and 5° with an accuracy rate of 99.2%, 96.1% and 93.5% respectively. Compared with traditional multi-point distributed sensors, this method is lower cost and easier to operate. The method proposed in this paper can find applications in distinguishing the status of certain structures, such as robotic arms and some disabled auxiliary equipment.https://ieeexplore.ieee.org/document/9511279/Multi-point bending sensorsensitized plastic fiberfiber specklegramdeep learningconvolutional neural network |
| spellingShingle | Shun Lu Zhongwei Tan Guangde Li Yang Jingya A Sensitized Plastic Fiber Sensor for Multi-Point Bending Measurement Based on Deep Learning IEEE Photonics Journal Multi-point bending sensor sensitized plastic fiber fiber specklegram deep learning convolutional neural network |
| title | A Sensitized Plastic Fiber Sensor for Multi-Point Bending Measurement Based on Deep Learning |
| title_full | A Sensitized Plastic Fiber Sensor for Multi-Point Bending Measurement Based on Deep Learning |
| title_fullStr | A Sensitized Plastic Fiber Sensor for Multi-Point Bending Measurement Based on Deep Learning |
| title_full_unstemmed | A Sensitized Plastic Fiber Sensor for Multi-Point Bending Measurement Based on Deep Learning |
| title_short | A Sensitized Plastic Fiber Sensor for Multi-Point Bending Measurement Based on Deep Learning |
| title_sort | sensitized plastic fiber sensor for multi point bending measurement based on deep learning |
| topic | Multi-point bending sensor sensitized plastic fiber fiber specklegram deep learning convolutional neural network |
| url | https://ieeexplore.ieee.org/document/9511279/ |
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